Classification of gastric emptying and orocaecal transit through artificial neural networks.

Classical quantification of gastric emptying (GE) and orocaecal transit (OCT) based on half-life time T$ _{50} $, mean gastric emptying time (MGET), orocaecal transit time (OCTT) or mean caecum arrival time (MCAT) can lead to misconceptions when analyzing irregularly or noisy data. We show that this is the case for gastrointestinal transit of control and of diabetic rats. Addressing this limitation, we present an artificial neural network (ANN) as an alternative tool capable of discriminating between control and diabetic rats through GE and OCT analysis.

Mathematical models applied to thyroid cancer.

Thyroid cancer is the most prevalent endocrine neoplasia in the world. The use of mathematical models on the development of tumors has yielded numerous results in this field and modeling with differential equations is present in many papers on cancer. In order to know the use of mathematical models with differential equations or similar in the study of thyroid cancer, studies since 2006 to date was reviewed.

Mathematical analysis and simulations involving chemotherapy and surgery on large human tumours under a suitable cell-kill functional response.

Dosage and frequency of treatment schedules are important for successful chemotherapy. However, in this work we argue that cell-kill response and tumoral growth should not be seen as separate and therefore are essential in a mathematical cancer model. This paper presents a mathematical model for sequencing of cancer chemotherapy and surgery.